{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "编写函数inter_cross实现交叉操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inter_cross(indiv_list, gene_list, prob):\n",
    "    \"\"\" 对染色体进行交叉操作 \"\"\"\n",
    "    gene_num = len(gene_list[0])\n",
    "    ready_index = list(range(len(gene_list)))\n",
    "    while len(ready_index) >= 2:\n",
    "        d1 = random.choice(ready_index)\n",
    "        ready_index.remove(d1)\n",
    "        d2 = random.choice(ready_index)\n",
    "        ready_index.remove(d2)\n",
    "        if np.random.uniform(0, 1) <= prob:\n",
    "            loc = random.choice(range(gene_num))\n",
    "            print(d1,d2,\"exchange loc --> \",loc)\n",
    "            # 对数据做交叉操作\n",
    "            if indiv_list is not None:\n",
    "                tmp = indiv_list[d1].iloc[:,loc]\n",
    "                indiv_list[d1].iloc[:,loc] = indiv_list[d2].iloc[:,loc]\n",
    "                indiv_list[d2].iloc[:,loc] = tmp\n",
    "                \n",
    "            # 对基因型做交叉操作\n",
    "            tmp = gene_list[d1][loc]\n",
    "            gene_list[d1][loc] = gene_list[d2][loc]\n",
    "            gene_list[d2][loc] = tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设有两个个体，它们的基因组分别为[g(add,X1,X2),g(log,X1),g(add,g(log,X2),X3)]、[g(pow2,X3),g(add,g(inv,X1),g(log,X2)),g(log,g(tanh,X4))]\n",
    "\n",
    "绘制对应的二叉树组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_tree(feature_string, title=None, node_size=5000, font_size=18):\n",
    "    my_dict = transform(feature_string)\n",
    "    root, labels, _ = bitree(my_dict, len(my_dict)-1, 0, labels={})\n",
    "    graph = nx.Graph()\n",
    "    graph, pos = create_graph(graph, root)\n",
    "    nx.draw_networkx(graph, pos, node_size=node_size,width=2,node_color='black',font_color='white',font_size=font_size,with_labels=True,labels=labels)\n",
    "    plt.axis('off')\n",
    "    if title is not None:\n",
    "        plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform(feature_string):\n",
    "    my_dict={}\n",
    "    pattern = r'g\\([^\\(\\)]*\\)'\n",
    "    so = re.search(pattern, feature_string)\n",
    "    while so:\n",
    "        start, end = so.span()\n",
    "        key = len(my_dict)\n",
    "        my_dict[key]=so.group()\n",
    "        feature_string = feature_string[0:start]+'<'+str(key)+'>'+feature_string[end:]\n",
    "        so = re.search(pattern, feature_string)\n",
    "    return my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bitree(mapping, start_no, index=0, labels={}):\n",
    "    name, left, right = parse(mapping[start_no])\n",
    "    if left is not None:\n",
    "        if type(left) == int:\n",
    "            left_node, s_labels, max_index = bitree(mapping, left, index+1, labels)\n",
    "            labels = s_labels\n",
    "        else:\n",
    "            left_node = Node(index+1, left)\n",
    "            labels[index+1] = left\n",
    "            max_index = index+1\n",
    "    else:\n",
    "        left_node = None\n",
    "    \n",
    "    if right is not None:\n",
    "        if type(right) == int:\n",
    "            right_node, s_labels, max_index = bitree(mapping, right, max_index+1, labels)\n",
    "            labels = s_labels\n",
    "        else:\n",
    "            right_node = Node(max_index+1, right)\n",
    "            labels[max_index+1] = right\n",
    "            max_index = max_index+1\n",
    "    else:\n",
    "        right_node = None\n",
    "        \n",
    "    labels[index] = name\n",
    "    return Node(index, name, left_node, right_node) ,labels, max_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse(group_unit):\n",
    "    tmp = group_unit.lstrip(\"g(\").rstrip(\")\").split(',')\n",
    "    tmp = tmp + [None] if len(tmp) == 2 else tmp\n",
    "    return [int(x[1:-1]) if x is not None and re.match(r'<[0-9]+>',x) else x for x in tmp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "    def __init__(self, value, label, left=None, right=None):\n",
    "        self.value = value\n",
    "        self.label = label\n",
    "        self.left = left  \n",
    "        self.right = right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_graph(G, node, pos={}, x=0, y=0, layer=1):\n",
    "    pos[node.value] = (x, y)\n",
    "    if node.left:\n",
    "        G.add_edge(node.value, node.left.value)\n",
    "        l_x, l_y = x - 1 / layer, y - 1\n",
    "        l_layer = layer + 1\n",
    "        create_graph(G, node.left, x=l_x, y=l_y, pos=pos, layer=l_layer)\n",
    "    if node.right:\n",
    "        G.add_edge(node.value, node.right.value)\n",
    "        r_x, r_y = x + 1 / layer, y - 1\n",
    "        r_layer = layer + 1\n",
    "        create_graph(G, node.right, x=r_x, y=r_y, pos=pos, layer=r_layer)\n",
    "    return G, pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import re\n",
    "# 以下 font.family 设置仅适用于 Mac系统，其它系统请使用对应字体名称\n",
    "matplotlib.rcParams['font.family'] = 'Arial Unicode MS'\n",
    "\n",
    "A = ['g(add,X1,X2)','g(log,X1)','g(add,g(log,X2),X3)']\n",
    "B = ['g(pow2,X3)','g(add,g(inv,X1),g(log,X2))','g(log,g(tanh,X4))']\n",
    "counter = 1\n",
    "titles=['个体A基因1','个体A基因2','个体A基因3','个体B基因1','个体B基因2','个体B基因3']\n",
    "plt.figure(figsize=(15,8))\n",
    "for e in A+B:\n",
    "    plt.subplot(2,3,counter)\n",
    "    plot_tree(e, title=titles[counter - 1],node_size=300,font_size=8)\n",
    "    counter = counter + 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用inter_cross函数进行交叉操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0 exchange loc -->  0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "\n",
    "inter_cross(None, [A,B], 1)\n",
    "counter = 1\n",
    "titles=['个体A基因1','个体A基因2','个体A基因3','个体B基因1','个体B基因2','个体B基因3']\n",
    "plt.figure(figsize=(15,8))\n",
    "for e in A+B:\n",
    "    plt.subplot(2,3,counter)\n",
    "    plot_tree(e, title=titles[counter - 1],node_size=300,font_size=8)\n",
    "    counter = counter + 1\n",
    "plt.show()\n",
    "# 0 1 exchange loc -->  1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "编写函数mutate实现变异操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mutate(indiv_list, gene_list, prob, input_data, featureIdx, nMax=10):\n",
    "    gene_num = len(gene_list[0])\n",
    "    ready_index = list(range(len(gene_list)))\n",
    "    for i in ready_index:\n",
    "        if np.random.uniform(0, 1) <= prob:\n",
    "            loc = random.choice(range(gene_num))\n",
    "            print(i,\"mutate on --> \",loc)\n",
    "            tmp = random_get_tree(input_data, featureIdx, nMax) \n",
    "            if indiv_list is not None:\n",
    "                indiv_list[i].iloc[:,loc] = tmp['f_value']\n",
    "            gene_list[i][loc] = tmp['tree_exp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于个体A，使用mutate实现变异，当发生变异时，绘制其变异前后二叉树组图。为了显示变异的效果，这里将突变概率设置为0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_get_tree(input_data,featureIdx,nMax=10):\n",
    "    \"\"\" \n",
    "    从原始数据特征中，随机获取特征表达树  \n",
    "    featureIdx: 原始特征的下标数值，最小从1开始\n",
    "    nMax:一次最多从特征中可放回抽样次数，默认为10\n",
    "    \"\"\"\n",
    "    data = pd.DataFrame({\"X\"+str(e):input_data.iloc[:,(e-1)].values for e in featureIdx})\n",
    "    \n",
    "    # 随机抽取N个特征下标\n",
    "    N = random.choice(range(2,nMax+1))\n",
    "    \n",
    "    # 随机决定是使用满二叉树还是偏二叉树\n",
    "    if random.choice([0,1]) == 1:\n",
    "        # 选择满二叉树\n",
    "        select_feature_index = [random.choice(featureIdx) for i in range(N)]+[0]*int(2**np.ceil(np.log2(N)) - N)\n",
    "        random.shuffle(select_feature_index)\n",
    "        select_feature_index = ['data.X'+str(e)+\".values\" if e> 0 else '0' for e in select_feature_index]\n",
    "        tree_exp = gen_full_tree_exp(select_feature_index)\n",
    "    else:\n",
    "        # 选择偏二叉树\n",
    "        select_feature_index = ['data.X'+str(e)+\".values\" for e in [random.choice(featureIdx) for i in range(N)]]\n",
    "        tree_exp =  gen_side_tree_exp(select_feature_index)\n",
    "    return {\"f_value\":eval(tree_exp),\"tree_exp\":tree_exp.replace(\"data.\",\"\").replace(\".values\",\"\")}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建偏二叉树，并生成数学表达式\n",
    "def gen_side_tree_exp(var_flag_array):\n",
    "    if len(var_flag_array) == 1:\n",
    "        return add_one_group(var_flag_array[0])\n",
    "    else:\n",
    "        var_flag_array[1] = 'g('+random.choice(two_group)+','+add_one_group(var_flag_array[0])+','+add_one_group(var_flag_array[1])+')'\n",
    "        del var_flag_array[0]\n",
    "        return gen_side_tree_exp(var_flag_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建满二叉树，并生成数学表达式\n",
    "def gen_full_tree_exp(var_flag_array):\n",
    "    half_n = len(var_flag_array)//2\n",
    "    middle_array = []\n",
    "    for i in range(half_n):\n",
    "        if var_flag_array[i] == '0' and var_flag_array[i+half_n] != '0':\n",
    "            middle_array.append('g('+random.choice(one_group)+','+add_one_group(var_flag_array[i+half_n])+')')\n",
    "        elif var_flag_array[i] != '0' and var_flag_array[i+half_n] == '0':\n",
    "            middle_array.append('g('+random.choice(one_group)+','+add_one_group(var_flag_array[i])+')')\n",
    "        elif var_flag_array[i] != '0' and var_flag_array[i+half_n] != '0':\n",
    "            middle_array.append('g('+random.choice(two_group)+','+add_one_group(var_flag_array[i])+','+add_one_group(var_flag_array[i+half_n])+')')\n",
    "    if len(middle_array) == 1:\n",
    "        return add_one_group(middle_array[0])\n",
    "    else:\n",
    "        return gen_full_tree_exp(middle_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_number = 0.01\n",
    "\n",
    "# 一元运算\n",
    "def log(x):\n",
    "    return np.sign(x)*np.log2(np.abs(x)+1)\n",
    "\n",
    "def sqrt(x):\n",
    "    return np.sqrt(x-np.min(x)+min_number)\n",
    "\n",
    "def pow2(x):\n",
    "    return x**2\n",
    "\n",
    "def pow3(x):\n",
    "    return x**3\n",
    "\n",
    "def inv(x):\n",
    "    return 1*np.sign(x)/(np.abs(x)+min_number)\n",
    "\n",
    "def sigmoid(x):\n",
    "    if np.std(x) < min_number:\n",
    "        return x\n",
    "    x = (x - np.mean(x))/np.std(x)\n",
    "    return (1 + np.exp(-x))**(-1)\n",
    "\n",
    "def tanh(x):\n",
    "    if np.std(x) < min_number:\n",
    "        return x\n",
    "    x = (x - np.mean(x))/np.std(x)\n",
    "    return (np.exp(x) - np.exp(-x))/(np.exp(x) + np.exp(-x))\n",
    "\n",
    "def relu(x):\n",
    "    if np.std(x) < min_number:\n",
    "        return x\n",
    "    x = (x - np.mean(x))/np.std(x)\n",
    "    return np.array([e if e > 0 else 0 for e in x])\n",
    "\n",
    "def binary(x):\n",
    "    if np.std(x) < min_number:\n",
    "        return x\n",
    "    x = (x - np.mean(x))/np.std(x)\n",
    "    return np.array([1 if e > 0 else 0 for e in x])\n",
    "\n",
    "# 二元运算\n",
    "def add(x,y):\n",
    "    return x + y\n",
    "\n",
    "def sub(x,y):\n",
    "    return x - y\n",
    "\n",
    "def times(x,y):\n",
    "    return x * y\n",
    "\n",
    "def div(x,y):\n",
    "    return x*np.sign(y)/(np.abs(y)+min_number)\n",
    "\n",
    "two_group = ['add', 'sub', 'times', 'div']\n",
    "one_group = ['log', 'sqrt', 'pow2', 'pow3', 'inv', 'sigmoid', 'tanh', 'relu', 'binary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机增加一元运算符\n",
    "def add_one_group(feature_string, prob=0.3):\n",
    "    return 'g('+random.choice(one_group)+','+feature_string+')' if np.random.uniform(0, 1) < prob else feature_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def g(f, a, b=None):\n",
    "    \"\"\"\n",
    "    f: 一元或二元运算函数\n",
    "    a: 第一个参数\n",
    "    b: 如果f是一元运算函数，则b为空，否则代表二元运算的第二个参数\n",
    "    \"\"\"\n",
    "    if b is None:\n",
    "        return f(a)\n",
    "    else:\n",
    "        return f(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "iris = pd.read_csv(\"http://image.cador.cn/data/iris.csv\")\n",
    "pre_A = A.copy()\n",
    "mutate(None,[A],0.9,iris,[1,2,3,4])\n",
    "# 0 mutate on -->  2\n",
    "counter = 1\n",
    "titles=['个体A基因1（变异前）','个体A基因2（变异前）','个体A基因3（变异前）','个体A基因1（变异后）','个体A基因2（变异后）','个体A基因3（变异后）']\n",
    "plt.figure(figsize=(15,8))\n",
    "for e in pre_A+A:\n",
    "    plt.subplot(2,3,counter)\n",
    "    plot_tree(e, title=titles[counter - 1],node_size=300,font_size=10)\n",
    "    counter = counter + 1\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
